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Category: performance

  • Top 30 AWS Cloud Interview Questions

    Preparing for an AWS Cloud interview? This comprehensive list of 30 key questions covers a wide range of AWS services and concepts, designed to help you demonstrate your understanding and expertise. 1. What is AWS? Answer: AWS (Amazon Web Services) is a comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from… Read more

  • Top 20 Databricks Interview Questions

    Preparing for a Databricks interview? This article compiles 20 key questions covering various aspects of the platform, designed to help you showcase your knowledge and skills. 1. What is Databricks? Answer: Databricks is a unified analytics platform built on top of Apache Spark. It provides a collaborative environment for data engineering, data science, and machine… Read more

  • Top 20 React Interview Questions and Answers

    This article presents 20 essential React interview questions with detailed answers, covering a range of fundamental concepts to help you prepare effectively. 1. What is React? Answer: React is a declarative, efficient, and flexible JavaScript library for building user interfaces (UIs) or UI components. It allows developers to create complex UIs from small and isolated… Read more

  • NodeJS Event loop

    Here we discuss a fundamental concept in Node.js: the Event Loop. In essence, the Event Loop is what allows Node.js to perform non-blocking I/O operations – despite JavaScript being single-threaded. Here’s a breakdown of what it is and why it’s so important: The Problem: Single-Threaded JavaScript and Blocking I/OJavaScript, by its nature in most browser… Read more

  • Benefits of Sharding

    Sharding matters significantly in distributed systems and databases for several crucial reasons: Scalability: Improved Performance: Enhanced Availability and Fault Tolerance: Optimized Resource Utilization: Data Locality and Compliance: Read more

  • Databricks Data Ingestion Samples

    Let’s explore some common Databricks data ingestion scenarios with code samples in PySpark (which is the primary language for data manipulation in Databricks notebooks). Before You Begin Set up your environment: Ensure you have a Databricks workspace and have attached a notebook to a running cluster. Configure access: Depending on the data source, you might… Read more

  • Databricks High level Concepts

    Databricks High-Level Concepts: A Detailed Overview Databricks High-Level Concepts: A Detailed Overview Databricks is a unified analytics platform built on top of Apache Spark, designed to simplify big data processing and machine learning. It provides a collaborative environment for data scientists, data engineers, and business analysts. Here’s a detailed overview of its key high-level concepts:… Read more

  • Monitoring Apache Kafka infrastructure using New Relic

    One can effectively monitor Apache Kafka infrastructure using New Relic through several methods: 1. Kafka On-Host Integration (Recommended for most self-managed Kafka deployments): 2. Java Agent (for monitoring Java-based Producers and Consumers): 3. OpenTelemetry (for a vendor-agnostic approach): 4. Kafka Connect New Relic Connector (for sending data from Kafka Connect to New Relic): Choosing the… Read more

  • Monitoring Apache Kafka using the ELK stack

    One can effectively monitor Apache Kafka infrastructure using the ELK stack (Elasticsearch, Logstash, Kibana). Here’s a breakdown of how to achieve this: 1. Data Collection: You have a few primary ways to get Kafka-related data into your ELK stack: 2. Data Processing (Logstash – Optional but Powerful): 3. Data Storage (Elasticsearch): 4. Data Visualization and… Read more

  • Kafka Monitoring Tools

    Lets look at various tools to monitor your Apache Kafka deployments. Here’s a breakdown of some popular options, including both open-source and commercial solutions: Key Metrics to Monitor: Before diving into specific tools, it’s important to understand what metrics are crucial for Kafka monitoring: Open-Source Kafka Monitoring Tools: Commercial Kafka Monitoring Tools: Choosing the Right… Read more

  • Autonomous Content Creation for Social Media Marketing using Agentic AI

    Here we implement agentic AI use case focusing on a creative and dynamic domain: Autonomous Content Creation for Social Media Marketing. Use Case: A marketing agency wants to automate the process of creating engaging content for various social media platforms for their clients. Instead of relying solely on human content creators, an agentic AI can… Read more

  • Agentic AI for Autonomous Bank Statement Analysis and Anomaly Detection

    Let’s implement a sample use case: An Agentic AI for Autonomous Bank Statement Analysis and Anomaly Detection. Use Case: A financial institution wants to automate the process of analyzing customer bank statements to identify potential fraudulent activities, unusual spending patterns, or financial distress indicators. Instead of relying solely on rule-based systems or manual review, an… Read more

  • Agentic AI Tools

    Agentic AI refers to a type of artificial intelligence system that can operate autonomously to achieve specific goals. Unlike traditional AI, which typically follows pre-programmed instructions, agentic AI can perceive its environment, reason about complex situations, make decisions, and take actions with limited or no direct human intervention. These systems often leverage large language models… Read more

  • Comparing various Time Series Databases

    A Time Series Database (TSDB) is a type of database specifically designed to handle sequences of data points indexed by time. This is in contrast to traditional relational databases that are optimized for transactional data and may not efficiently handle the unique characteristics of time-stamped data. Here’s a comparison of key aspects of Time Series… Read more

  • The Monolith to Microservices Journey: Empowered by AI

    The transition from a monolithic application architecture to a microservices architecture, offers significant advantages. However, it can also be a complex and resource-intensive undertaking. The integration of Artificial Intelligence (AI) and Machine Learning (ML) offers powerful tools and techniques to streamline, automate, and optimize various stages of this journey, making it more efficient, less risky,… Read more

  • The Monolith to Microservices Journey: A Phased Approach to Architectural Evolution

    The transition from a monolithic application architecture to a microservices architecture is a significant undertaking, often driven by the desire for increased agility, scalability, resilience, and maintainability. A monolith, with its tightly coupled components, can become a bottleneck to innovation and growth. Microservices, on the other hand, offer a decentralized approach where independent services communicate… Read more

  • Navigating the Currents of Change: A Comprehensive Guide to Application Modernization

    In today’s rapidly evolving digital landscape, businesses face a constant imperative to adapt and innovate. At the heart of this transformation lies the need to modernize their core software applications. These applications, often the backbone of operations, can become impediments to growth and agility if left to stagnate. Application modernization is not merely about updating… Read more

  • Parquet “Indexing”

    While Parquet itself doesn’t have traditional database-style indexes that you explicitly create and manage, it leverages its columnar format and metadata to optimize data retrieval, which can be considered a form of implicit indexing. When it comes to joins, Parquet’s efficiency can significantly impact join performance in data processing frameworks. Here’s a breakdown of Parquet… Read more

  • Broadcast Hash Join

    The Broadcast Hash Join is a join optimization strategy used in distributed data processing frameworks like Apache Spark, Dask, and others. It’s particularly effective when one of the tables being joined is significantly smaller than the other and can fit into the memory of each executor node in the cluster. Here’s how it works: Algorithm:… Read more

  • Detail of Parquet

    The Parquet format is a column-oriented data storage format designed for efficient data storage and retrieval. It is an open-source project within the Apache Hadoop ecosystem. Here’s a breakdown of its key aspects: Key Characteristics: Advantages of Using Parquet: Disadvantages of Using Parquet: Parquet vs. Other Data Formats: In summary, Parquet is a powerful and… Read more

  • Medallion Architecture

    The Medallion Architecture is a data lakehouse architecture pattern popularized by Databricks. It’s designed to progressively refine data through a series of layers, ensuring data quality and suitability for various downstream consumption needs. The name “Medallion” refers to the distinct quality levels achieved at each layer, similar to how medals signify different levels of achievement.… Read more

  • Data Lake vs. Data Lakehouse: Understanding Modern Data Architectures

    Organizations today grapple with ever-increasing volumes and varieties of data. To effectively store, manage, and analyze this data, different architectural approaches have emerged. Two prominent concepts in this landscape are the data lake and the data lakehouse. While both aim to provide a centralized data repository, they differ significantly in their design principles and capabilities.… Read more

  • Building a Product Manual Chatbot with Amazon OpenSearch and Open-Source LLMs

    This article guides you through building an intelligent chatbot that can answer questions based on your product manuals, leveraging the power of Amazon OpenSearch for semantic search and open-source Large Language Models (LLMs) for generating informative responses. This approach provides a cost-effective and customizable solution without relying on Amazon Bedrock. The Challenge: Navigating through lengthy… Read more

  • Scaling a vector database

    Scaling a vector database is a crucial consideration as your data grows and your query demands increase. Here’s a breakdown of the common strategies and factors involved in scaling vector databases: Why Scaling is Important: Common Scaling Strategies: Techniques for Horizontal Scaling: Factors to Consider When Scaling: Choosing the Right Scaling Strategy: The best scaling… Read more

  • Automating Customer Communication: Building a Production-Ready LangChain Agent for Order Notifications

    In the fast-paced world of e-commerce, proactive and timely communication with customers is paramount for fostering trust and ensuring a seamless post-purchase experience. Manually tracking new orders and sending confirmation emails can be a significant drain on resources and prone to delays. This article presents a comprehensive guide to building a production-ready LangChain agent designed… Read more

  • Loading and Indexing data into a vector database

    Vector databases store data as high-dimensional vectors, which are numerical representations of data points. Loading data into a vector database involves converting your data into these vector embeddings. Indexing is a crucial step that follows loading, as it organizes these vectors in a way that allows for efficient similarity searches.Here’s a breakdown of the process: Read more

  • Spring AI chatbot with RAG and FAQ

    Demonstrate the concepts of building a Spring AI chatbot with both general knowledge RAG and an FAQ section into a single comprehensive article.Building a Powerful Spring AI Chatbot with RAG and FAQLarge Language Models (LLMs) offer incredible potential for building intelligent chatbots. However, to create truly useful and context-aware chatbots, especially for specific domains, we… Read more

  • Vector Database Internals

    Vector databases are specialized databases designed to store, manage, and efficiently query high-dimensional vectors. These vectors are numerical representations of data, often generated by machine learning models to capture the semantic meaning of the underlying data (text, images, audio, etc.). Here’s a breakdown of the key internal components and concepts: 1. Vector Embeddings: 2. Data… Read more

  • RAG with locally running LLM

    Sample code to enable running the LLM locally. This will involve using a local LLM instead of OpenAI. Key Changes: To run this code with a local LLM: Important Considerations: Read more

  • Retrieval Augmented Generation (RAG) with LLMs

    Retrieval Augmented Generation (RAG) is a technique that enhances the capabilities of Large Language Models (LLMs) by enabling them to access and incorporate information from external sources during the response generation process. This approach addresses some of the inherent limitations of LLMs, such as their inability to access up-to-date information or domain-specific knowledge. How RAG… Read more